Principal Engineer, AI Storage, Google Distributed Cloud

About Google Distributed Cloud (GDC)

With Google Distributed Cloud (GDC), we are creating the industry’s private and hybrid cloud offering that brings our customers Google Cloud’s AI-led services and infrastructure on an on-premise platform that is simple, secure, and scalable. We are creating AI capabilities and enabling modernization with a Kubernetes based consistent and open developer experience from edge to cloud that serve government and enterprise customers across the globe.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Role Overview: Principal Engineer, AI Storage, Google Distributed Cloud

As a Principal Engineer, AI Storage, Google Distributed Cloud, you will drive the architecture, design, and technology strategy for AI storage within Google Distributed Cloud. This role involves understanding complex AI model dynamics, optimizing infrastructure for training, fine-tuning, and inferencing, and collaborating with AI labs like Google DeepMind to enhance distributed cloud capabilities for on-premise deployments.

Minimum Qualifications

• Bachelor's degree in Engineering, Computer Science, or related technical field or equivalent practical experience.
• 15 years of professional experience.
• Experience leading technical innovation in AI labs, AI storage companies, hyperscalers, or directly related AI companies.
• Experience delivering models, agent, other, AI services, or AI infrastructure.
• Experience delivering hyperscale cloud services to enterprise customers.

Preferred Qualifications

• Experience with Artificial Intelligence, Machine Learning, and Generative AI.
• Experience with cloud services and platform development.
• Experience working with partners and developing relationships.
• Experience with “big picture” strategy thinking, enabling teams to deliver management solutions that are effective at scale.
• Ability to simply explain complex business or technical challenges.
• Ability to operate in a complex, fast-moving environment, collaborating across multiple teams and functions within and beyond the company.

Responsibilities

• Drive the architecture, design, and technology strategy that enables the best customer value in terms of price/performance, security, and reliability, for on-prem air-gapped and connected deployments.
• Understand how models work and are changing (i.e., moving to memory, reasoning, and AGI), how customers should choose the best model for their use case, how the infrastructure can be optimized to run the model, how integration with data and security come together, and how to work with AI labs (including Google DeepMind) to drive model enhancements for customers requiring a distributed cloud on-prem.
• Understand infrastructure requirements for training, fine-tuning, and inferencing, with particular technical knowledge in storage architectures and on-prem landscape, including Lustre, VAST, Weka, etc.

Key Skills/Competency

• AI/Machine Learning
• Generative AI
• Cloud Services
• Storage Architectures
• Kubernetes
• Hyperscale Computing
• Technical Leadership
• Strategic Thinking
• Distributed Cloud
• On-premise Solutions